AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring

Autor/inn/en	Nye, Benjamin D.; Graesser, Arthur C.; Hu, Xiangen
Titel	AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring
Quelle	24 (2014), S.427-469 (43 Seiten)Infoseite zur Zeitschrift PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1007/s40593-014-0029-5
Schlagwörter	Intelligent Tutoring Systems; Natural Language Processing; Computer Software; Artificial Intelligence; Teaching Methods; Logical Thinking; Questioning Techniques; Cooperative Learning; Reading Comprehension; Reading Strategies; Psychological Patterns; Metacognition; Animation; Demonstrations (Educational); Hypermedia; Computer Simulation; Concept Mapping; Semantics; Programming + Suchen Sie Ihr Suchwort? Intelligentes Tutorsystem; Natürliche Sprache; Künstliche Intelligenz; Teaching method; Lehrmethode; Unterrichtsmethode; Befragungstechnik; Fragetechnik; Kooperatives Lernen; Leseverstehen; Reading strategy; Leselernstufe; Lesetechnik; Meta cognitive ability; Meta-cognition; Metakognitive Fähigkeit; Metakognition; Demonstrationsexperiment; Demonstrationsmodell; Demonstrationsunterricht; Computergrafik; Computersimulation; Concept Map; Semantik; Programmierung
Abstract	AutoTutor is a natural language tutoring system that has produced learning gains across multiple domains (e.g., computer literacy, physics, critical thinking). In this paper, we review the development, key research findings, and systems that have evolved from AutoTutor. First, the rationale for developing AutoTutor is outlined and the advantages of natural language tutoring are presented. Next, we review three central themes in AutoTutor's development: human-inspired tutoring strategies, pedagogical agents, and technologies that support natural-language tutoring. Research on early versions of AutoTutor documented the impact on deep learning by co-constructed explanations, feedback, conversational scaffolding, and subject matter content. Systems that evolved from AutoTutor added additional components that have been evaluated with respect to learning and motivation. The latter findings include the effectiveness of deep reasoning questions for tutoring multiple domains, of adapting to the affect of low-knowledge learners, of content over surface features such as voices and persona of animated agents, and of alternative tutoring strategies such as collaborative lecturing and vicarious tutoring demonstrations. The paper also considers advances in pedagogical agent roles (such as trialogs) and in tutoring technologies, such semantic processing and tutoring delivery platforms. This paper summarizes and integrates significant findings produced by studies using AutoTutor and related systems. [The article was published in "International Journal of Artificial Intelligence in Education" (EJ1042132).] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01